ModeNet: Mode Selection Network For Learned Video Coding
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Abstract (arXiv)
In this paper, a mode selection network (ModeNet) is proposed to enhance deep learning-based video compression. Inspired by traditional video coding, ModeNet purpose is to enable competition among several coding modes. The proposed ModeNet learns and conveys a pixel-wise partitioning of the frame, used to assign each pixel to the most suited coding mode. ModeNet is trained alongside the different coding modes to minimize a rate-distortion cost. It is a flexible component which can be generalized to other systems to allow competition between different coding tools. Mod-eNet interest is studied on a P-frame coding task, where it is used to design a method for coding a frame given its prediction. ModeNet-based systems achieve compelling performance when evaluated under the Challenge on Learned Image Compression 2020 (CLIC20) P-frame coding track conditions.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 6 Jul 2020.
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Citing papers per year: 2023: 1; 2024: 2.
3 papers.
- 2023MMVC: Learned Multi-Mode Video Compression with Block-based Prediction Mode Selection and Density-Adaptive Entropy Codingcited by 9
- 2024Prediction and Reference Quality Adaptation for Learned Video Compressioncited by 5
- 2024Survey on Visual Signal Coding and Processing with Generative Models: Technologies, Standards and Optimizationcited by 2
- 2024Prediction and Reference Quality Adaptation for Learned Video Compression20 Jun 2024
- 2024Survey on Visual Signal Coding and Processing with Generative Models: Technologies, Standards and Optimization23 May 2024
- 2023MMVC: Learned Multi-Mode Video Compression with Block-based Prediction Mode Selection and Density-Adaptive Entropy Coding5 Apr 2023